Surveillance in one form or another has been around the oil and gas industry for many years. The scope was initially limited to a manual process using individual engineering and business applications to evaluate a limited number of wells based either on their value or their trouble status. At one time, a hardcopy report, monthly target, production number, downtime, and operating expenses were adequate for our needs. Data was gathered by hand and manually manipulated before presenting them to operations managers. If a problem was identified, engineers performed an analysis based on their experience and judgment using the tools at hand. Because resources were limited, only the most important wells, or those with serious problems, could be studied, and even this examination often lacked depth. Many opportunities for better performance and risk reduction were missed or bypassed (Nikolaou et al. 2006).

More recently, major improvements in surveillance, including more engineering and operations content, were introduced to oil and gas production. Mature fields, such as Elk Hills in California, have implemented sophisticated monitoring centers that feed data into real-time displays, enabling operations staff to see the status of key measurements. Green fields, such as Agbami (offshore Nigeria), have deployed many model-based, integrated workflows to automate and facilitate operational excellence (Sankaran et al. 2011).

The improvements have led to a new role and definition for surveillance. When most people speak of surveillance today, they no longer assume that it is a passive information delivery system. The addition of advanced analytics, expert systems, and process automation (all of which routinely leverage real-time information) have taken surveillance to a new level, combining business or operational intelligence with automated technical calculations. The result of these advances is a new generation of hybrid solutions incorporating elements of "data-driven" methods, including management by exception (MBE), business intelligence (BI), and situational awareness (SA) with "model-driven" techniques, such as model-based decision support (MBDS), advanced process control (APC), and consequential analysis (CA). State-of-the-art information technology tools are used to enable more efficient traditional processes, build single-purpose workflow applications, and deploy fully-automated intelligent systems using the latest automation, models, and control systems.

The goal of future surveillance systems should be to replace monitoring wells against a target with managing production assets against their potential in a safe, environmentally-responsible way.

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